Personalizable Pen-Based Interface Using Life-Long Learning

Abdullah Almaksour 1, * Eric Anquetil 1 Solen Quiniou 2 Mohamed Cheriet 2
* Auteur correspondant
1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
2 ETS - Ecole de Technologie Supérieure [Montréal]
Abstract : In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system, that can be integrated into an application to provide personalization capabilities. Experiments on an on-line gesture database were performed by considering various user personalization scenarios. The experiments show that the proposed evolving gesture recognition system continuously adapts and evolve according to new data of learned classes, and remains robust when introducing new unseen classes, at any moment during the lifelong learning process.
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Communication dans un congrès
International Conference on Frontiers in Handwriting Recognition (ICFHR), Aug 2010, Kolkata, India. pp.188-193, 2010
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Abdullah Almaksour, Eric Anquetil, Solen Quiniou, Mohamed Cheriet. Personalizable Pen-Based Interface Using Life-Long Learning. International Conference on Frontiers in Handwriting Recognition (ICFHR), Aug 2010, Kolkata, India. pp.188-193, 2010. 〈hal-00582441〉

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